74 citations · 74 across the 4 of their papers we have counts for
7 papers
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling
Alessio Fallani, Ramil Nugmanov, Jose Arjona-Medina +3
We evaluate the impact of pretraining Graph Transformer architectures on atom-level quantum-mechanical features for the modeling of absorption, distribution, metabolism, excretion,…
Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models
Jose Arjona-Medina, Ramil Nugmanov
Despite the rapid and significant advancements in deep learning for Quantitative Structure-Activity Relationship (QSAR) models, the challenge of learning robust molecular represent…
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling
Julius Ott, Lorenzo Servadei, Jose Arjona-Medina +7
Data selection is essential for any data-based optimization technique, such as Reinforcement Learning. State-of-the-art sampling strategies for the experience replay buffer improve…
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER
Markus Holzleitner, Lukas Gruber, José Arjona-Medina +2
We prove under commonly used assumptions the convergence of actor-critic reinforcement learning algorithms, which simultaneously learn a policy function, the actor, and a value fun…
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Vihang P. Patil, Markus Hofmarcher, Marius-Constantin Dinu +5
Reinforcement learning algorithms require many samples when solving complex hierarchical tasks with sparse and delayed rewards. For such complex tasks, the recently proposed RUDDER…
Explaining and Interpreting LSTMs
Leila Arras, Jose A. Arjona-Medina, Michael Widrich +5
While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the v…